Multi-objective optimal control of an air-to-water heat pump for residential heating

Verhelst, Clara; Degrauwe, David; Logist, Filip; Impe, Jan Van; Helsen, Lieve

doi:10.1007/s12273-012-0061-z

Cited by 28 publications

(17 citation statements)

References 23 publications

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“…Many previous works evaluate the techno-economic benefit [25,26,27,28,29,30,31], i.e, the objective functions are performances over a year, or multiple years. Operational performances are evaluated at a much shorter period, such as a day, 24h.…”

Section: Introductionmentioning

confidence: 99%

Optimal control of a wind–PV-hybrid powered heat pump water heater

2017

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This paper develops an optimal control (OC) model of a heat pump water heater (HPWH) supplied by a wind generator-photovoltaic-grid system. The objective function is energy cost minimization, taking into account the time-of-use electricity tariff (TOU), which is an important control parameter. The control variables are the supply switch to the HPWH and the power from the grid, while the hot water temperature inside the tank is the state variable. The model meets both the HPWH's technical and operational constraints in providing hot water at a desired temperature and achieves load shifting. This problem is solved using a mixed integer linear program. The results show a 70.7% cost reduction upon implementation of this intervention. A case study is done and the OC shows significant potential in energy and cost saving when compared to the digital thermostat controller used currently in most HPWHs. The economic analysis is presented in this paper as well. Nomenclature P w (t) wind generator power output (kW) P pv (t) photovoltaic power output (kW) P g (t)grid power (kW) P hp heat pump water heater power demand (kW) COP coefficient of performance u(t) heat pump power supply switch control variable (0 or 1) T (t) hot water temperature inside the tank ( • C) T low and T up lower and upper hot water temperature set points (

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Section: Introductionmentioning

confidence: 99%

Optimal control of a wind–PV-hybrid powered heat pump water heater

2017

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“…Alot of success in optimal design and control of HPWHs in the last decades has increased their application even at domestic building levels [15]. Though, few challenges exist in providing the hot water at short time interval and the initial cost [16,17]. Integration of such equipment with REs is the main challenge [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Optimal control of a fuel cell/wind/PV/grid hybrid system with thermal heat pump load

2016

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This paper presents an optimal energy management strategy for a grid tied photovoltaic-wind-fuel cell hybrid power supply system. The hybrid systems meets the load demand consisting of an electrical load and a heat pump water heater supplying thermal load. The objective is to minimize energy cost and maximize fuel cell output, taking into account the time-of-use electricity tariff. The optimal control problem is solved using a mixed integer linear program with the supply switch to the heat pump water heater and the power from the grid, power to/from inverter, power to electrolyzer and from fuel cell the control variables. The hot water temperature inside the water storage tank and the hydrogen in the storage tank are the state variables. The performance of the proposed control strategy is tested by simulating different operating scenarios: with and without renewable energy feed-in, and the results confirm the effectiveness of the proposed control strategy, as it increases the supply reliability of the system. Keywords

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“…According to [11], the coefficient of performance (COP) of a heat pump can be approximated with a quadratic function. Since only the quantity 1 is used in the objective function (to obtain the electrical power used by the heat pump system), a linear model of this quantity is derived: The COP is defined as the ratio between the thermal power : or MN: and the electricity consumption of the heat pump 8Q .…”

Section: E Heat Pump Performancementioning

confidence: 99%

“…In the present study, three different objectives have been studied: minimization of the thermal energy delivered by the heat pump with ;Ž• = •# , minimization of the electricity used by the heat pump with ;Ž• = •-, and minimization of the cost of the electricity use with ;Ž• = <;e$ . These terms are explicitly described in (9) to (11). •# considers directly the minimization of the control actions : and MN: , in a similar fashion than for instance [5]:…”

Section: Optimal Control Problem Formulationmentioning

confidence: 99%

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Configurations of model predictive control to exploit energy flexibility in building thermal loads

Péan

Salom

Costa‐Castelló

2018

2018 IEEE Conference on Decision and Control (CDC)

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A model predictive control (MPC) framework is developed in the present study, with the final objective to improve the energy flexibility of building thermal loads through demand-side management. Three different configurations are tested and tuned, with the following objective functions: minimizing the delivered energy to the building, the electrical energy used by the HVAC system (heat pump) or the cost of this electricity use. To validate these MPC configurations, a Matlab-Trnsys co-simulator is also created, in order to run the MPC on a virtual plant composed of a detailed building model. The MPC strategy manages to run effectively on the chosen study case (a residential building with heat pump in Spain), and the differences between configurations are discussed.

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Multi-objective optimal control of an air-to-water heat pump for residential heating

Abstract: This document contains the post-print pdf-version of the refereed paper: "Multi-objective optimal control of an air-to-water heat pump for residential heating."

Cited by 28 publications

References 23 publications

Optimal control of a wind–PV-hybrid powered heat pump water heater

Optimal control of a wind–PV-hybrid powered heat pump water heater

Optimal control of a fuel cell/wind/PV/grid hybrid system with thermal heat pump load

Configurations of model predictive control to exploit energy flexibility in building thermal loads

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